Many invasive and opportunistic pests cause multiple, interdependent adverse outcomes on agricultural production. Often, however, these impacts are modeled independently, which can bias empirical inferences and contribute to inaccurate recommendations. We use a copula function to more accurately model the joint behavior and provide an empirical example of its application to assess the impacts of the wheat stem sawfly (WSS). We use a unique farm-level dataset to estimate the expected losses associated with WSS and then evaluate two popular WSS management strategies. We find that strategies minimizing long-run infestation levels are preferred to those that seek to maximize yield potential in exchange for higher risk of intertemporal infestation.
While nearly instantaneous commodity futures price information provides price forecasts for national markets, many market participants are interested in forecasts of local cash prices. Expected basis estimates are often used to convert futures prices into local price forecasts. This study considers basis patterns in the northern U.S. hard red spring and hard red winter wheat markets. Using data on basis values across 215 grain-handling facilities, we empirically test the forecasting capabilities of numerous basis models. Contrary to basis models developed for other U.S. regions, we show that recent futures prices, protein content, and harvest information are more important for accurate basis forecasts than historical basis averages. The preferred basis models are used to develop an automated web-based basis forecasting tool, available at http : //wheatbasis.montana.edu.
Soybean rust is a highly mobile infectious disease and can be transmitted across short and long distances. Soybean rust is estimated to cause yield losses that can range between 1%-25%. An analysis of spatio-temporal infection risks within the United States is performed through the use of a unique data set. Observations from over 35,000 field-level inspections between 2005 and 2007 are used to conduct a county-level analysis. Statistical inferences are derived by employing zero-inflated Poisson and negative binomial models. In addition, the model is adjusted to account for potential endogeneity between inspections and soybean rust finds. Past soybean rust finds and inspections in the county and in the surrounding counties, weather and overwintering conditions, and plant maturity groups and planting dates are all found to be significant factors determining soybean rust. These results are then used to accordingly price annual insurance contracts or indemnification programs that cover soybean rust damages.